"""Frozen model architecture and user-tunable inference configuration."""

from __future__ import annotations

import json
from dataclasses import asdict, dataclass
from pathlib import Path


@dataclass(frozen=True)
class MDiffAEConfig:
    """Frozen model architecture config. Stored alongside weights as config.json."""

    in_channels: int = 3
    patch_size: int = 16
    model_dim: int = 896
    encoder_depth: int = 4
    decoder_depth: int = 4
    bottleneck_dim: int = 64
    mlp_ratio: float = 4.0
    depthwise_kernel_size: int = 7
    adaln_low_rank_rank: int = 128
    # VP diffusion schedule endpoints
    logsnr_min: float = -10.0
    logsnr_max: float = 10.0
    # Pixel-space noise std for VP diffusion initialization
    pixel_noise_std: float = 0.558
    # Token mask ratio for PDG (fraction of spatial tokens replaced with mask_feature)
    pdg_mask_ratio: float = 0.75

    def save(self, path: str | Path) -> None:
        """Save config as JSON."""
        p = Path(path)
        p.parent.mkdir(parents=True, exist_ok=True)
        p.write_text(json.dumps(asdict(self), indent=2) + "\n")

    @classmethod
    def load(cls, path: str | Path) -> MDiffAEConfig:
        """Load config from JSON."""
        data = json.loads(Path(path).read_text())
        return cls(**data)


@dataclass
class MDiffAEInferenceConfig:
    """User-tunable inference parameters with sensible defaults.

    PDG is very sensitive in mDiffAE — use small strengths (1.05–1.2).
    """

    num_steps: int = 1  # decoder forward passes (NFE)
    sampler: str = "ddim"  # "ddim" or "dpmpp_2m"
    schedule: str = "linear"  # "linear" or "cosine"
    pdg_enabled: bool = False
    pdg_strength: float = 1.1
    seed: int | None = None